AI Robotics, 6 credits (TDDE05)

AI-robotik, 6 hp

Main field of study

Computer Science and Engineering Computer Science

Level

Second cycle

Course type

Programme course

Examiner

Cyrille Berger

Director of studies or equivalent

Peter Dalenius

Available for exchange students

Yes
Course offered for Semester Period Timetable module Language Campus VOF
6CDDD Computer Science and Engineering, M Sc in Engineering 8 (Spring 2018) 1, 2 4, 4 English Linköping v
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2018) 1, 2 4, 4 English Linköping v
6CITE Information Technology, M Sc in Engineering 8 (Spring 2018) 1, 2 4, 4 English Linköping v
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2018) 1, 2 4, 4 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering 8 (Spring 2018) 1, 2 4, 4 English Linköping v
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2018) 1, 2 4, 4 English Linköping v
6MDAV Computer Science, Master's Programme 2 (Spring 2018) 1, 2 4, 4 English Linköping v
6MICS Computer Science, Master's Programme 2 (Spring 2018) 1, 2 4, 4 English Linköping v
6MICS Computer Science, Master's Programme (AI and Data Mining) 2 (Spring 2018) 1, 2 4, 4 English Linköping v

Main field of study

Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Computer Science and Engineering, M Sc in Engineering
  • Information Technology, M Sc in Engineering
  • Computer Science and Software Engineering, M Sc in Engineering
  • Computer Science, Master's Programme

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.

Prerequisites

An introductory AI course, Object-oriented programming (preferably in C++ or Python.)

Intended learning outcomes

The aim of this course is to give an overview of the use of Artificial Intelligence (AI) techniques for robotic systems, through the use of simulated robot, actual hardware and widely used software packages, such as the Robot Operating System (ROS). The main focus of the course is for student to learn how the different components that constitute a robot: perception, control and deliberation interact with each other to form an autonomous system, the course will have an emphasis on how such a system take decision to accomplish its goals.
After the course, the student will be able to:

  • to list and explain important problems and techniques in the area of AI robotics,
  • to use existing frameworks to develop an autonomous robot, and
  • to design, implement and evaluate the algorithms needed to provide autonomous functionality to a robot in a simulated environment, and
  • to transpose simulated tests to actual hardware, and
  • to make written and oral presentations of their work. 

Course content

Perception and Scene Interpretation. Navigation: Localisation and path planning Autonomy and Levels of autonomy. Control and Decision-Making. Behavior-based robotics. Robotic Programming. Reactive, Deliberative and Hybrid robot architectures. Human-Robot Interraction 

Teaching and working methods

Through a serie of labs (~1 month), the students develop/integrate basic robotic functionality, so that in the end, it is a system that can move, avoid obstacle and take basic decision. The system will be improved during the project phase. Each student pick a topic related to AI Robotic (among a selected list of topics), implement and evaluate the algorithm, and write a report, with a description of the algorithm. Students are expected to present their individual work during a seminar and during a group seminar they will present their robotic system. During a lab session, they should demonstrate to the assistant the functionalities of their robot. Students work in group of 5-6.
The course runs over the entire spring semester.
 

Examination

LAB1Laboratory workU, G2 credits
PRA1Project assignmentU, 3, 4, 54 credits

Grades

Four-grade scale, LiU, U, 3, 4, 5

Other information

Supplementary courses:
Automated Planning, Sensor fusion, Computer Vision, Control Theory, Multi-Agents.
 

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Peter Dalenius

Examiner

Cyrille Berger

Education components

Preliminary scheduled hours: 0 h
Recommended self-study hours: 160 h
There is no course literature available for this course.
LAB1 Laboratory work U, G 2 credits
PRA1 Project assignment U, 3, 4, 5 4 credits

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